Scientists found that tuning into joyful or soft music can cut motion sickness by over half, offering a simple brain-based solution to nausea during travel.
Study: A study on the mitigating effect of different music types on motion sickness based on EEG analysis. Image credit: Estrada Anton/Shutterstock.com
A recent study published in Frontiers in Human Neuroscience examined the impact of various types of music on motion sickness. Scientists found that listening to certain types of music, particularly joyful and soft music, can effectively alleviate motion sickness symptoms and improve travel experience.
Motion sickness: Assessments and interventions
The advancement in autonomous driving technology has increased driving convenience and safety. However, recent studies have also documented increased motion sickness incidence, particularly among passengers.
During mixed-mode operation, i.e., alternating between manual and assisted driving, passengers frequently experience motion sickness. This happens when vehicle movements, like turning or accelerating, don’t match what the eyes see, leading to a mismatch between balance and vision.
Currently, motion sickness is assessed through both subjective and objective measurements. In subjective measurements, the person is asked to complete relevant questionnaires, such as the Motion Sickness Questionnaire (MSQ), the Misery Scale (MISC), and the Simulator Sickness Questionnaire (SSQ), which help generate ratings for motion sickness.
In contrast, objective measurements rely on physiological signal acquisition equipment that provides objective physiological and behavioral data from patients. This data is correlated to the level of motion sickness experienced by the person.
Researchers have developed pharmacological management strategies and sensory interventions to mitigate motion sickness. Motion sickness has been associated with the expression of cholinergic M1, M2, and M5 receptor subtypes in the vestibular organ and vestibular ganglion. Considering this research, anti-motion sickness drugs (e.g., scopolamine) have been developed that block M1 and M5 receptor subtypes.
Not many studies have assessed the efficacy of olfactory- or tactile-based interventions, particularly the association between auditory stimuli (e.g., music genres) and motion sickness in a driving environment.
About the study
The current study aimed to address the research gap and evaluate the effects of various music genres on motion sickness. A motion sickness recognition model was constructed based on electroencephalographic signals, which enabled systematic assessment of differential regulatory effects of four types of music, including joyful, stirring, sad, and soft, on motion sickness.
A driving simulation experiment was designed instead of real-road experiments to induce symptoms of motion sickness. Electroencephalogram (EEG) data were collected from the participants. Driving simulators induced a visual-vestibular conflict effect similar to that in a real-motion environment.
The software allowed users to select ten roads with varying road complexity, surrounding landforms, and lengths. All these roads required more than five minutes of travel time to complete. During this road-screening phase, the MISC, KSS, and Likert Scale assessed the participants' motion sickness.
Forty volunteers (22 males and 18 females) were enrolled in this initial screening. From these, 30 participants with moderate susceptibility to motion sickness were selected for the main music-relief experiment.
Each participant in the main experiment was exposed to one of six conditions: four music types (joyful, sad, stirring, soft), one natural recovery control group, and one baseline group. When motion sickness reached three or more, it was considered a successful induction of motion sickness.
Multidimensional features of five brain regions were extracted from the EEG signals, frequency domain features, and instantaneous domain statistical features. These features can comprehensively characterize brain activity's temporal and spatial dynamics in the motion sickness state. An optimal classification prediction model was developed by comparing the performance of traditional machine learning methods and deep learning models in recognizing motion sickness states. This model included BP neural network (BPNN), plain Bayes (NB), K nearest neighbour (KNN), support vector machine (SVM), and logistic regression (LR).
Study findings
The five models' average accuracy, recall, and precision scores achieved the maximum in the occipital lobe area. This finding suggests that EEG signals in this area are closely associated with motion sickness.
Based on the predictive accuracy, the current study selected the BPNN model for occupant motion sickness recognition in the occipital lobe area. After 1980 iterations of training, the BPNN model achieved the highest accuracy of 85.6% in the test set. For further modulation analysis, the completed motion sickness recognition model was classified by the BPNN algorithm using EEG features trained under the occipital lobe area.
The effect of each music (modulation group) and natural recovery (control group) intervention on motion sickness was assessed. A correlation was found between the participants' subjective relief scores and the objective relief scores based on EEG data. Compared to natural recovery, the subjective scores indicated that soft and joyful music could effectively alleviate motion sickness symptoms by 57.3% and 56.7%, respectively.
It must be noted that sad music had a lower alleviation effect (40%) than natural recovery (43.3%), while stirring music showed moderate effects, better than natural recovery in objective EEG data but not in subjective reports.
Kolmogorov-Chaitin (KC) complexity of EEG signals was closely related to mental fatigue. The current study observed that when the participants were calm, the overall KC values were high in the occipital lobe. After motion sickness, the KC complexity in the occipital lobe area had a lower value. A significant negative correlation was recorded between motion sickness and EEG KC complexity in the occipital lobe area.
Conclusions
The current study developed an effective motion sickness recognition model using EEG signal analysis and machine learning strategies. Soft and joyful music significantly alleviated symptoms of motion sickness, while sad music worsened them, and stirring music had mixed effects depending on the measure used.
The authors noted limitations, including the relatively small sample size, the use of a simulator rather than real driving, and the narrow age range of participants. They emphasized that future research must validate these results in larger groups and real-world driving environments.
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